Behavior and cognition depend on the integrated action of neural structures and populations distributed throughout the brain. We recently developed a set of molecular imaging tools that enable multiregional processing and plasticity in neural networks to be studied at a brain-wide scale in rodents and nonhuman primates. Here we will describe how a novel genetically encoded activity reporter enables information flow in virally labeled neural circuitry to be monitored by fMRI. Using the reporter to perform functional imaging of synaptically defined neural populations in the rat somatosensory system, we show how activity is transformed within brain regions to yield characteristics specific to distinct output projections. We also show how this approach enables regional activity to be modeled in terms of inputs, in a paradigm that we are extending to address circuit-level origins of functional specialization in marmoset brains. In the second part of the talk, we will discuss how another genetic tool for MRI enables systematic studies of the relationship between anatomical and functional connectivity in the mouse brain. We show that variations in physical and functional connectivity can be dissociated both across individual subjects and over experience. We also use the tool to examine brain-wide relationships between plasticity and activity during an opioid treatment. This work demonstrates the possibility of studying diverse brain-wide processing phenomena using molecular neuroimaging.

Fly Escape Behaviors: Flexible and Modular We have identified a set of escape maneuvers performed by a fly when confronted by a looming object. These escape responses can be divided into distinct behavioral modules. Some of the modules are very stereotyped, as when the fly rapidly extends its middle legs to jump off the ground. Other modules are more complex and require the fly to combine information about both the location of the threat and its own body posture. In response to an approaching object, a fly chooses some varying subset of these behaviors to perform. We would like to understand the neural process by which a fly chooses when to perform a given escape behavior. Beyond an appealing set of behaviors, this system has two other distinct advantages for probing neural circuitry. First, the fly will perform escape behaviors even when tethered such that its head is fixed and neural activity can be imaged or monitored using electrophysiology. Second, using Drosophila as an experimental animal makes available a rich suite of genetic tools to activate, silence, or image small numbers of cells potentially involved in the behaviors. Neural Circuits for Escape Until recently, visually induced escape responses have been considered a hardwired reflex in Drosophila. White-eyed flies with deficient visual pigment will perform a stereotyped middle-leg jump in response to a light-off stimulus, and this reflexive response is known to be coordinated by the well-studied giant fiber (GF) pathway. The GFs are a pair of electrically connected, large-diameter interneurons that traverse the cervical connective. A single GF spike results in a stereotyped pattern of muscle potentials on both sides of the body that extends the fly's middle pair of legs and starts the flight motor. Recently, we have found that a fly escaping a looming object displays many more behaviors than just leg extension. Most of these behaviors could not possibly be coordinated by the known anatomy of the GF pathway. Response to a looming threat thus appears to involve activation of numerous different neural pathways, which the fly may decide if and when to employ. Our goal is to identify the descending pathways involved in coordinating these escape behaviors as well as the central brain circuits, if any, that govern their activation. Automated Single-Fly Screening We have developed a new kind of high-throughput genetic screen to automatically capture fly escape sequences and quantify individual behaviors. We use this system to perform a high-throughput genetic silencing screen to identify cell types of interest. Automation permits analysis at the level of individual fly movements, while retaining the capacity to screen through thousands of GAL4 promoter lines. Single-fly behavioral analysis is essential to detect more subtle changes in behavior during the silencing screen, and thus to identify more specific components of the contributing circuits than previously possible when screening populations of flies. Our goal is to identify candidate neurons involved in coordination and choice of escape behaviors. Measuring Neural Activity During Behavior We use whole-cell patch-clamp electrophysiology to determine the functional roles of any identified candidate neurons. Flies perform escape behaviors even when their head and thorax are immobilized for physiological recording. This allows us to link a neuron's responses directly to an action.

Reflex circuits in the nervous system integrate changes in the environment with physiology. Compact clusters of brain neuron cell bodies, termed nuclei, are essential for receiving sensory input and for transmitting motor outputs to the body. These nucelii are critical relay stations which process incoming information and convert these signals to outgoing action potentials which regulate immune system functions. Thus, reflex neural circuits maintain parameters of immunological physiology within a narrow range optimal for health. Advances in neuroscience and immunology using optogenetics, pharmacogenetics, and functional mapping offer a new understanding of the importance of neural circuitry underlying immunity, and offer direct paths to new therapies.

In nature, sensory inputs are often highly structured, and statistical regularities of these signals can be extracted to form expectation about future sensorimotor associations, thereby optimizing behavior. One of the fundamental questions in neuroscience concerns the neural computations that underlie these probabilistic sensorimotor processing. Through a recurrent neural network (RNN) model and human psychophysics and electroencephalography (EEG), the present study investigates circuit mechanisms for processing probabilistic structures of sensory signals to guide behavior. We first constructed and trained a biophysically constrained RNN model to perform a series of probabilistic decision-making tasks similar to paradigms designed for humans. Specifically, the training environment was probabilistic such that one stimulus was more probable than the others. We show that both humans and the RNN model successfully extract information about stimulus probability and integrate this knowledge into their decisions and task strategy in a new environment. Specifically, performance of both humans and the RNN model varied with the degree to which the stimulus probability of the new environment matched the formed expectation. In both cases, this expectation effect was more prominent when the strength of sensory evidence was low, suggesting that like humans, our RNNs placed more emphasis on prior expectation (top-down signals) when the available sensory information (bottom-up signals) was limited, thereby optimizing task performance. Finally, by dissecting the trained RNN model, we demonstrate how competitive inhibition and recurrent excitation form the basis for neural circuitry optimized to perform probabilistic information processing.

For many animals, the sense of olfaction plays a major role in controlling sexual behaviors. Olfaction helps animals to detect mates, discriminate their status, and ultimately, decide on their behavioral output such as courtship behavior or aggression. Specific pheromone cues and receptors have provided a useful model to study how sensory inputs are converted into certain behavioral outputs. With the aid of recent advances in tools to record and manipulate genetically defined neurons, our understanding of the neural basis of sexual and social behavior has expanded substantially. I will discuss the current understanding of the neural processing of sex pheromones and the neural circuitry which controls sexual and social behaviors and ultimately reproduction, by focusing on rodent studies, mainly in mice, and the vomeronasal sensory system.

Empathy plays a critical role in social interactions, and many species, including rodents, display evolutionarily conserved behavioral antecedents of empathy. In both humans and rodents, the anterior cingulate cortex (ACC) encodes information about the affective state of others. However, little is known about which downstream targets of the ACC contribute to empathy behaviors. We optimized a protocol for the social transfer of pain behavior in mice and compared the ACC-dependent neural circuitry responsible for this behavior with the neural circuitry required for the social transfer of two related states: analgesia and fear. We found that a 1-hour social interaction between a bystander mouse and a cagemate experiencing inflammatory pain led to congruent mechanical hyperalgesia in the bystander. This social transfer led to activation of neurons in the ACC and several downstream targets, including the nucleus accumbens (NAc), which was revealed by monosynaptic rabies virus tracing to be directly connected to the ACC. Bidirectional manipulation of activity in ACC-to-NAc inputs influenced the acquisition of socially transferred pain. Further, the social transfer of analgesia also depended upon ACC-NAc inputs. By contrast, the social transfer of fear instead required activity in ACC projections to the basolateral amygdala. This shows that mice rapidly adopt the sensory-affective state of a social partner, regardless of the valance of the information (pain, fear, or pain relief). We find that the ACC generates specific and appropriate empathic behavioral responses through distinct downstream targets. More sophisticated understanding of evolutionarily conserved brain mechanisms of empathy will also expedite the development of new therapies for the empathy-related deficits associated with a broad range of neuropsychiatric disorders.

One of the best established recurrent inhibitory pathways is the recurrent inhibition of mammalian motoneurons through Renshaw cells. Golgi cells form an inhibitory feedback circuit in the granular layer of cerebellum. Feedback inhibitory pathways are long established “textbook” elements of neural circuitry, but in both cases their functional role has not been well established. Here I will present some new observations on the function of recurrent inhibition in the spinal-cord, supporting the idea that this connection frequency tunes transmission of inputs through motoneurons. Secondly, I will discuss evidence that the function of Golgi cells is much more complex than classical studies based on circuit connectivity suggest.

Autobiographical memories are the ghosts of our past. Through them we visit places long departed, see faces once familiar, and hear voices now silent. These, often decades-old, personal experiences can be recalled on a whim or come unbidden into our everyday consciousness. Autobiographical memories are crucial to cognition because they facilitate almost everything we do, endow us with a sense of self and underwrite our capacity for autonomy. They are often compromised by common neurological and psychiatric pathologies with devastating effects. Despite autobiographical memories being central to everyday mental life, there is no agreed model of autobiographical memory retrieval, and we lack an understanding of the neural mechanisms involved. This precludes principled interventions to manage or alleviate memory deficits, and to test the efficacy of treatment regimens. This knowledge gap exists because autobiographical memories are challenging to study – they are immersive, multi-faceted, multi-modal, can stretch over long timescales and are grounded in the real world. One missing piece of the puzzle concerns the millisecond neural dynamics of autobiographical memory retrieval. Surprisingly, there are very few magnetoencephalography (MEG) studies examining such recall, despite the important insights this could offer into the activity and interactions of key brain regions such as the hippocampus and ventromedial prefrontal cortex. In this talk I will describe a series of MEG studies aimed at uncovering the neural circuitry underpinning the recollection of autobiographical memories, and how this changes as memories age. I will end by describing our progress on leveraging an exciting new technology – optically pumped MEG (OP-MEG) which, when combined with virtual reality, offers the opportunity to examine millisecond neural responses from the whole brain, including deep structures, while participants move within a virtual environment, with the attendant head motion and vestibular inputs.

Electronic interfaces to the retina represent an exciting development in science, engineering, and medicine – an opportunity to exploit our knowledge of neural circuitry and function to restore or even enhance vision. However, although existing devices demonstrate proof of principle in treating incurable blindness, they produce limited visual function. Some of the reasons for this can be understood based on the precise and specific neural circuitry that mediates visual signaling in the retina. Consideration of this circuitry suggests that future devices may need to operate at single-cell, single-spike resolution in order to mediate naturalistic visual function. I will show large-scale multi-electrode recording and stimulation data from the primate retina indicating that, in some cases, such resolution is possible. I will also discuss cases in which it fails, and propose that we can improve artificial vision in such conditions by incorporating our knowledge of the visual system in bi-directional devices that adapt to the host neural circuitry. Finally, I will introduce the Stanford Artificial Retina Project, aimed at developing a retinal implant that more faithfully reproduces the neural code of the retina, and briefly discuss the implications for scientific investigation and for other neural interfaces of the future.